Challenge
- Applying AI algorithms to develop Dynamic Pricing for ticketing.
- Development of the AI model based on historical data and a growing set of variables applicable to the model.
Solution
- Implementation of the Dynamic Price Simulator platform, which combines in-depth knowledge of the price dynamics of the different offers with an analytical component that helps understand pricing policy and make better decisions on it, for both match tickets and season tickets.
- Machine & Deep Learning.
- Models developed with Python and R, with scientific validation.
Result
- Higher ticket sales volume.
- Higher business margins.
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